Method and device for determining crop sowing time, storage medium and processor

Abstract

The embodiment of the invention provides a method and device for determining crop sowing time, a storage medium and a processor. The method comprises the following steps: acquiring crop information of crops to be sown in a planting area; based on the meteorological data of the planting area in the experience sowing time of the to-be-sown crop, determining a date meeting a preset condition in the experience sowing time, so as to obtain a suitable sowing time of the to-be-sown crop in the planting area; determining a growth period time sequence for the to-be-sown crop based on the crop variety and the meteorological data of the suitable sowing period; based on the growth period time sequence, the meteorological data of the suitable sowing period and climate quality models corresponding to the crop varieties, determining a climate quality index sequence of the planting area in the suitable sowing period, the climate quality model of each crop variety being constructed based on the strongly correlated meteorological data of each growth period of the crop variety in the historical time period; and determining the date corresponding to the maximum climatic quality index as the optimal sowing time of the to-be-sown crop in the planting area.

Description

Technical Field

[0001] The present application relates to the field of agricultural planting technology, and in particular to a method, device, system, storage medium and processor for determining crop sowing time. Background Art

[0002] Rice is the most widely planted crop in China with the highest total output, and it plays a leading role in food production and consumption. As the living standards of the Chinese people continue to improve, the quality of rice has gradually become a key factor pursued by consumers. In recent years, against the backdrop of global climate change, the frequency of agricultural meteorological disasters such as drought, floods, and high temperature and heat damage has increased significantly, resulting in great volatility in the quality of crops, which has had a certain impact on farmers' income and even national food security.

[0003] The quality of rice is mainly affected by factors such as the duration and intensity of light during different growth periods, maximum temperature, minimum temperature, daily temperature difference, and precipitation during its growth and development process. In previous studies, a recommendation method for the appropriate sowing period of rice was established based on the growth and development characteristics of rice during the sowing and emergence periods. However, these recommendations for the appropriate sowing period only considered the meteorological conditions before and after the sowing date. This is mainly to avoid the seedlings from being affected by low temperature and cold damage. It is beneficial in the early stages of crop growth and does not take into account the changes in rice quality caused by different meteorological conditions throughout the entire growth period of rice. There are certain limitations that make it impossible to improve the quality of rice. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a method for determining the sowing time of crops, so as to solve the technical defect that the method for recommending the suitable sowing period of crops in the prior art does not take into account the changes in rice quality caused by different meteorological conditions during the entire growth period of rice, resulting in the inability to improve rice quality.

[0005] In order to achieve the above-mentioned object, the first aspect of the present application provides a method for determining a crop sowing time, comprising:

[0006] Acquire crop information of crops to be sown in the planting area, the crop information including the crop variety and growth period of the crops to be sown;

[0007] Determine the date that meets the preset conditions in the empirical sowing period based on the meteorological data of the planting area during the empirical sowing period of the crop to be sown, so as to obtain the appropriate sowing period for the crop to be sown in the planting area;

[0008] Determine the growth period time series for the crops to be sown based on the crop varieties and meteorological data of the suitable sowing period;

[0009] Based on the growth period time series, the meteorological data of the suitable sowing period and the climate quality model corresponding to the crop variety, the climate quality index sequence of the planting area in the suitable sowing period is determined. For any crop variety, the climate quality model of the crop variety is constructed based on the strongly correlated meteorological data of each growth period of the crop variety in the historical time period.

[0010] The date corresponding to the maximum climate quality index is determined as the optimal sowing time for the crops to be sown in the planting area.

[0011] In an embodiment of the present application, the meteorological data includes daily precipitation, daily wind level and average daily temperature, and the date that meets the preset conditions in the empirical sowing period is determined based on the meteorological data of the planting area during the empirical sowing period of the crop to be sown, so as to obtain the suitable sowing period for the crop to be sown in the planting area, including: obtaining the daily precipitation, daily wind level and average daily temperature for each date in the empirical sowing period; for any date, if the daily precipitation of the date is less than a first value, the daily wind level of the date is less than a second value, and the difference between the daily average temperature of the date and the daily average temperature of multiple consecutive adjacent dates of the date is greater than a third value, it is determined that the date meets the preset conditions; the date whose empirical sowing period meets the preset conditions is determined as the suitable sowing period for the crop to be sown in the planting area.

[0012] In the embodiment of the present application, for any crop variety, the climate quality model of the crop variety is shown in formula (1):

[0013] Q=∑r s (β 0s +β 1s x 1s +β 2s x 2s +…+β ns x ns +ε) (1)

[0014] Among them, Q is the climate quality index of crop varieties, β is , i∈(1,n) is the regression coefficient, x is , i∈(1,n) is the strongly correlated meteorological data of the crop variety during its growth period s, ε is a high-order infinitesimal error term, r s is the contribution proportion corresponding to the growth period s of the crop variety.

[0015] In an embodiment of the present application, for any crop variety, the method further includes: obtaining historical planting information of the crop variety, the historical planting information including historical meteorological data of the crop variety in multiple historical time periods, multiple growth periods, sub-historical time periods corresponding to each growth period, and quality parameters in each historical time period, wherein each historical time period corresponds to a complete growth cycle of the crop variety, and the historical meteorological data includes the average daily temperature, the highest daily temperature, the lowest daily temperature, and the number of sunshine hours of the crop variety in each historical time period; for each historical time period, determining the contribution ratio of each growth period of the crop variety, and determining the comprehensive climate quality index of the crop variety in the historical time period based on the quality parameters of the crop variety, Determine the stage climate quality comprehensive index corresponding to each growth period based on the contribution ratio of each growth period and the climate quality comprehensive index; determine multiple average meteorological data corresponding to each growth period; for any growth period in any historical time period, determine the strongly correlated meteorological data of the growth period based on the stage climate quality comprehensive index of the growth period, multiple average meteorological data, the stage climate quality comprehensive index of all growth periods and historical meteorological data; determine the regression coefficient and high-order infinitesimal error term corresponding to each growth period based on the strongly correlated meteorological data of each growth period and the stage climate quality comprehensive index; construct a climate quality model for crop varieties based on the regression coefficient, high-order infinitesimal error term and contribution ratio corresponding to each growth period.

[0016] In the embodiment of the present application, the strongly correlated meteorological data of each growth period is determined according to formula (2):

[0017] ρ=cov(Q(s),x) / (σ Q(s) *σx)(2)

[0018] Among them, ρ is the correlation coefficient, Q(s) is the comprehensive index of climate quality corresponding to any growth period, x is any average meteorological data of any growth period, σ Q(s) is the standard deviation of the comprehensive index of stage climate quality of all growing periods in multiple historical time periods, and σx is the standard deviation of any historical meteorological data of all growing periods in multiple historical time periods.

[0019] In an embodiment of the present application, for any average meteorological data of any growth period, when the correlation coefficient corresponding to the average meteorological data is greater than a preset value, the average meteorological data is determined as strongly correlated meteorological data of the growth period.

[0020] In the embodiment of the present application, the regression coefficient and the high-order infinitesimal error term corresponding to each growth period are determined according to formula (3):

[0021] Q(s)=β0+β1X1+β2X2+...+β n X n+ε(3)

[0022] Among them, β i , i∈(1,n) is the regression coefficient corresponding to any growth period, Q(s) is the comprehensive index of climate quality corresponding to any growth period, ε is the high-order infinitesimal error term corresponding to any growth period, x i ,i∈(1,n) is the strongly correlated meteorological data corresponding to any growth period.

[0023] A second aspect of the present application provides a device for determining a crop sowing time, comprising:

[0024] a memory configured to store instructions;

[0025] The processor is configured to call the instructions from the memory and implement the above-mentioned method for determining the crop sowing time when executing the instructions.

[0026] A third aspect of the present application provides a system for determining crop sowing time, comprising the device for determining crop sowing time according to the above.

[0027] A fourth aspect of the present application provides a machine-readable storage medium having instructions stored thereon, which, when executed by a processor, configure the processor to execute the above-mentioned method for determining the sowing time of crops.

[0028] The above technical solution provides a method for determining the sowing time of crops. By using meteorological data, the suitability of meteorological conditions is evaluated for the traditional empirical sowing period, thereby excluding sowing dates with unsuitable climatic conditions and obtaining suitable sowing dates with suitable climatic conditions. Subsequently, the remaining suitable sowing dates are traversed using a climate quality model for each crop variety based on strongly correlated meteorological data of each historical growth period to determine the sowing time that can achieve the best climate quality. Not only does it fully consider and utilize the meteorological conditions of each growth period, but it can also more accurately deduce the best sowing time for each crop variety, thereby achieving the purpose of improving crop quality.

[0029] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings:

[0031] Figure 1 A schematic diagram of a process for determining crop sowing time according to an embodiment of the present application is shown;

[0032] Figure 2 A schematic diagram of a process of another method for determining crop sowing time according to an embodiment of the present application is shown;

[0033] Figure 3 The internal structure diagram of a computer device according to an embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0035] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0036] Figure 1 The following is a schematic diagram showing a flow chart of a method for determining crop sowing time according to an embodiment of the present application. Figure 1 As shown, an embodiment of the present application provides a method for determining crop sowing time, which may include the following steps.

[0037] Step 101, obtaining crop information of crops to be sown in a planting area, wherein the crop information includes the crop variety and growth period of the crops to be sown.

[0038] In the embodiment of the present application, it should be noted that the planting area may refer to an agricultural area used for agricultural production activities, generally centered on farming, mainly planting food crops and cash crops, also known as an agricultural area. Crops refer to crops planted in agricultural areas, such as rice, wheat, soybeans, etc. Before planting, the processor needs to obtain the planting area and the crop information to be sown in the planting area, and the crop information includes the crop variety and growth period of the crop to be sown. Among them, the crop variety may refer to the type of crop and the rice type, such as rice, wheat, or soybeans. For example, when the crop is rice, the rice type can be divided into one-season medium rice, one-season late rice, double-season early rice, double-season late rice, regenerated rice, etc. The rice type refers to the rice system, which refers to the system divided according to the number of times that can be planted and harvested in a year in the same farmland. It should be noted that the time from the emergence of crops to the maturity of crops is called the growth period. The growth period of crops, generally speaking, is the time from sowing to maturity. Taking rice as an example, the growth period of rice can be divided into seven stages: seedling stage, green stage, tillering stage, panicle differentiation stage, booting stage, heading stage, and maturity stage.

[0039] Step 102, based on the meteorological data of the planting area during the empirical sowing period of the crop to be sown, determine the date that meets the preset conditions in the empirical sowing period to obtain the appropriate sowing period for the crop to be sown in the planting area.

[0040] In the embodiment of the present application, it should be noted that the empirical sowing period may refer to a time range period during a long planting process, which is obtained by combining planting experience with historical data, climate characteristics, soil conditions, crop growth cycle and other factors, and is more suitable for sowing a certain variety of crops. Among them, in the present technical scheme, the empirical sowing period should include a suitable sowing period and an actual sowing period. For example, taking rice as an example, different rice varieties have different requirements for temperature conditions when sowing, and generally have different empirical sowing periods. Therefore, it is necessary to obtain the empirical sowing period of the variety of rice according to the information such as the variety of rice planted and the region where it is located. At this time, after the empirical sowing period is obtained, it does not mean that every date in the empirical sowing period is suitable for sowing. The sowing of crops is affected by actual meteorological data. Therefore, the processor needs to further obtain the meteorological data of the planting area during the empirical sowing period of the crops to be sown. Specifically, the meteorological forecast data for each day within the long range of the empirical sowing period can be obtained through weather forecast, wherein, if there is no long-term meteorological forecast data, short-term forecast data can be used in combination with the historical average meteorological data of the planting area as a substitute. After acquiring the meteorological data, the processor traverses all dates included in the empirical sowing period, determines whether the weather conditions on each date are suitable for sowing the crops to be sown, retains the dates that meet the preset conditions in the weather conditions, and removes the dates that do not meet the preset conditions, so as to obtain the suitable sowing period for the crops to be sown in the planting area. It should be noted that the meteorological data may include but is not limited to precipitation, wind force level, temperature, etc. The preset conditions can be set according to historical experience. For example, taking the meteorological data as precipitation as an example, the daily precipitation of a certain date less than 5 mm can be set to meet the preset conditions.

[0041] In an embodiment of the present application, the meteorological data includes daily precipitation, daily wind level and average daily temperature. Based on the meteorological data of the planting area during the empirical sowing period of the crop to be sown, the date that meets the preset conditions in the empirical sowing period is determined to obtain the suitable sowing period for the crop to be sown in the planting area, including: obtaining the daily precipitation, daily wind level and average daily temperature for each date in the empirical sowing period; for any date, if the daily precipitation of the date is less than a first value, the daily wind level of the date is less than a second value, and the difference between the daily average temperature of the date and the daily average temperature of multiple consecutive adjacent dates of the date is greater than a third value, it is determined that the date meets the preset conditions; the date whose empirical sowing period meets the preset conditions is determined as the suitable sowing period for the crop to be sown in the planting area.

[0042] In this embodiment, after obtaining the meteorological data, the processor traverses all dates included in the empirical sowing period to determine whether the weather conditions of each date are suitable for sowing the crops to be sown, wherein, in this technical solution, the meteorological data may include daily precipitation, daily wind force level and daily average temperature. Therefore, the processor can obtain the daily precipitation, daily wind force level and daily average temperature of each date in the empirical sowing period, and screen the suitable sowing period by judging whether the daily precipitation, daily wind force level and daily average temperature of each date meet the preset conditions. Specifically, the first value, the second value and the third value can be set according to historical experience and actual needs, for example, the first value can be set to 5mm, the second value can be set to level 4, and the third value can be set to 12°C. Therefore, for any date, when the daily precipitation on the date is less than 5mm, the daily wind force level is less than level 4, and the sliding difference between the average temperatures of multiple consecutive adjacent dates of the date is greater than 12°C, the date can be determined as a suitable sowing period for sowing the crops to be sown. Among them, multiple consecutive adjacent dates can be set to 3 days, that is, the sliding range between this date and the average temperature of the next 3 consecutive days is greater than 12°C.

[0043] Step 103, determining the growth period time series for the crops to be sown based on the crop varieties and the meteorological data of the suitable sowing period.

[0044] In the embodiments of the present application, it should be noted that each crop includes multiple growth periods, and the time interval corresponding to each growth period is continuous and different. Different varieties of crops include different growth periods. Therefore, the growth period time series corresponding to each crop is different. In the present technical solution, the growth period time series for the crops to be sown can be determined based on the crop variety and the meteorological data of the suitable sowing period. Specifically, after the meteorological data is obtained, the time range of the suitable sowing period is traversed, and the growth period time series of the crops to be sown is obtained by the accumulated temperature method. It should be noted that since the obtained meteorological data is meteorological forecast data, the obtained growth period time series should be the predicted growth period time series. Specifically, taking rice as an example, the obtained time series of the growth period of rice can be: seedling stage: 0405-0416, greening stage: 0416-0508, tillering stage: 0508-0517, panicle differentiation stage: 0517-0603, booting stage: 0603-0617, heading stage: 0617-0708, maturity stage: 0708-0801, where the numbers represent the range of months and days.

[0045] Step 104, based on the growth period time series, the meteorological data of the suitable sowing period and the climate quality model corresponding to the crop variety, determine the climate quality index sequence of the planting area during the suitable sowing period, wherein, for any crop variety, the climate quality model of the crop variety is constructed based on the strongly correlated meteorological data of each growth period of the crop variety in the historical time period.

[0046] In the embodiment of the present application, after obtaining the growth period time series of the crops to be sown in the planting area, the meteorological data of the suitable sowing period, and the crop varieties, the climate quality index sequence of the planting area in the suitable sowing period can be determined based on the growth period time series, the meteorological data of the suitable sowing period, and the climate quality model corresponding to the crop varieties. It should be noted that in the present technical solution, each crop variety has a corresponding climate quality model, and the climate quality models are constructed based on the strongly correlated meteorological data of each growth period of the crop variety in the historical time period.

[0047] In the embodiment of the present application, for any crop variety, the climate quality model of the crop variety is shown in formula (1):

[0048] Q=∑r s (β 0s +β 1s x 1s +β 2s x 2s +...+β ns x ns +ε) (1)

[0049] Among them, Q is the climate quality index of crop varieties, β is , i∈(1,n) is the regression coefficient, x is , i∈(1,n) is the strongly correlated meteorological data of the crop variety during its growth period s, ε is a high-order infinitesimal error term, r s is the contribution proportion corresponding to the growth period s of the crop variety.

[0050] In this embodiment, it should be noted that the regression coefficient, the high-order infinitesimal error term, and the contribution ratio are all constants.

[0051] In an embodiment of the present application, for any crop variety, the method further includes: obtaining historical planting information of the crop variety, the historical planting information including historical meteorological data of the crop variety in multiple historical time periods, multiple growth periods, sub-historical time periods corresponding to each growth period, and quality parameters in each historical time period, wherein each historical time period corresponds to a complete growth cycle of the crop variety, and the historical meteorological data includes the average daily temperature, the highest daily temperature, the lowest daily temperature, and the number of sunshine hours of the crop variety in each historical time period; for each historical time period, determining the contribution proportion of each growth period of the crop variety, and determining the comprehensive climate quality index of the crop variety in the historical time period based on the quality parameters of the crop variety, Determine the stage climate quality comprehensive index corresponding to each growth period based on the contribution ratio of each growth period and the climate quality comprehensive index; determine multiple average meteorological data corresponding to each growth period; for any growth period in any historical time period, determine the strongly correlated meteorological data of the growth period based on the stage climate quality comprehensive index of the growth period, multiple average meteorological data, the stage climate quality comprehensive index of all growth periods and historical meteorological data; determine the regression coefficient and high-order infinitesimal error term corresponding to each growth period based on the strongly correlated meteorological data of each growth period and the stage climate quality comprehensive index; construct a climate quality model for crop varieties based on the regression coefficient, high-order infinitesimal error term and contribution ratio corresponding to each growth period.

[0052] In this embodiment, it should be noted that each crop variety has a corresponding climate quality model, and the climate quality models are constructed based on the strongly correlated meteorological data of each growth period of the crop variety in the historical time period. Specifically, the construction of the climate quality model of each crop variety includes, for any crop variety, first, obtaining the historical meteorological data of the crop variety, multiple growth periods, sub-historical time periods corresponding to each growth period, and quality parameters in each historical time period. Among them, each historical time period corresponds to a complete growth cycle of the crop variety, and the historical meteorological data include the average daily temperature, maximum daily temperature, minimum daily temperature and sunshine hours of the crop variety in each historical time period. Specifically, the historical meteorological data also includes daily radiation intensity. For each historical time period, the contribution ratio of each growth period of the crop variety can be determined based on historical experience values. Taking rice as an example, the growth period of rice includes the seedling stage, greening stage, tillering stage, panicle differentiation stage, booting stage, heading stage and maturity stage in sequence. The contribution ratios of the seedling stage, greening stage, tillering stage, panicle differentiation stage, booting stage, heading stage and maturity stage can be 1:1:1:1:3:3:1.

[0053] In this embodiment, it should be noted that the quality parameter may refer to a parameter used to characterize the quality of the crop, and specifically, the quality parameter may include but is not limited to head rice yield (f1), chalkiness (f2), amylose content (f3), taste value (f4), etc. Among them, head rice yield (HRY or F1) may refer to the proportion of intact grains in the processed rice, chalkiness (CH or F2) may refer to the proportion of white or translucent areas inside the rice, amylose content (AC or F3) is a type of starch, amylose content may refer to the content of amylose in rice, and taste value (TV or F4) may refer to the taste and flavor of rice, which is usually evaluated by professional judges through tasting. Based on the quality parameters of crop varieties, the climate quality comprehensive index of crop varieties in the historical time period can be determined. For example, taking rice as an example, the climate quality comprehensive index Q of crop varieties in a historical time period can be obtained by weighting and summing all quality parameters. The weighted formula can be: Q = 0.1*f1 + 0.28*f2 + 0.56*f3 + 0.06*f4. Under normal circumstances, the larger the Q value, the better the climate quality of rice. When it is greater than a certain threshold, it can be regarded as excellent quality. For any historical time period, after obtaining the climate quality comprehensive index of crop varieties in the historical time period, the stage climate quality comprehensive index corresponding to each growth period can be determined according to the contribution ratio of each growth period of the crop variety and the climate quality comprehensive index. Specifically, the stage climate quality comprehensive index corresponding to each growth period can be obtained based on the product between the contribution ratio of each growth period and the climate quality comprehensive index, that is, it can be obtained according to the formula Q*r = Q(s), where Q is the climate quality comprehensive index, r is the contribution ratio of each growth period, and Q(s) is the stage climate quality comprehensive index corresponding to each growth period.

[0054] In the embodiment of the present application, the strongly correlated meteorological data of each growth period is determined according to formula (2):

[0055] ρ=cov(Q(s),x) / (σ Q(s) *σx)(2)

[0056] Among them, ρ is the correlation coefficient, Q(s) is the comprehensive index of climate quality corresponding to any growth period, x is any average meteorological data of any growth period, σ Q(s) is the standard deviation of the comprehensive index of stage climate quality of all growing periods in multiple historical time periods, and σx is the standard deviation of any historical meteorological data of all growing periods in multiple historical time periods.

[0057] In this embodiment, it should be noted that for any growth period within any historical time period, the historical meteorological data of each historical date within the time range of the growth period is screened, and for any historical meteorological data, the historical meteorological data of each date within the time range is summed and averaged, so that the average meteorological data of the historical meteorological data within the growth period can be obtained. For example, taking the historical meteorological data as the historical daily average temperature and the corresponding time range of the growth period as 10 days as an example, the historical daily average temperature of these 10 days is summed and averaged, and the historical average daily temperature of the growth period can be obtained.

[0058] In this embodiment, by substituting each average meteorological data in each growth period into formula (2), the correlation coefficient ρ between each average meteorological data and the growth period can be obtained. According to the value of the correlation coefficient ρ, it can be judged whether the average meteorological data is the strongly correlated meteorological data of the growth period.

[0059] In an embodiment of the present application, for any average meteorological data of any growing period, when the correlation coefficient corresponding to the average meteorological data is greater than a preset value, the average meteorological data is determined as strongly correlated meteorological data of the growing period.

[0060] In this embodiment, it should be noted that the preset value can be set according to an empirical value, for example, it can be set to 0.6. Therefore, for any average meteorological data of any growing period, when the value of the correlation coefficient ρ is greater than 0.6, the average meteorological data can be determined as strongly correlated meteorological data of the growing period.

[0061] In the embodiment of the present application, the regression coefficient and the high-order infinitesimal error term corresponding to each growth period are determined according to formula (3):

[0062] Q(s)=β0+β1X1+β2X2+...+β n X n +ε(3)

[0063] Among them, β i , i∈(1,n) is the regression coefficient corresponding to any growth period, Q(s) is the comprehensive index of climate quality corresponding to any growth period, ε is the high-order infinitesimal error term corresponding to any growth period, x i ,i∈(1,n) is the strongly correlated meteorological data corresponding to any growth period.

[0064] In this embodiment, it should be noted that for all growing periods in all historical time periods, the comprehensive index of stage climate quality corresponding to each growing period and the strongly correlated meteorological data of each growing period obtained according to formula (2) are substituted into formula (3) to obtain the regression coefficient and high-order infinitesimal error term corresponding to each growing period.

[0065] Step 105, determining the date corresponding to the maximum climate quality index as the optimal sowing time for the crops to be sown in the planting area.

[0066] In the embodiment of the present application, after determining the climate quality index sequence of the planting area in the suitable sowing period based on the growth period time series of the crops to be sown in the planting area, the meteorological data of the suitable sowing period, and the climate quality model corresponding to the crop varieties, the date corresponding to the maximum climate quality index is determined as the optimal sowing time of the crops to be sown in the planting area. The optimal sowing time is pushed to the corresponding farmers so that the farmers can perform the corresponding sowing operation according to the reference to the optimal sowing time.

[0067] In this embodiment, it should be noted that before the sowing operation is completed, the recommendation of the optimal sowing time needs to be updated accordingly according to the update frequency of the weather forecast data. Since changes in forecast data may cause changes in the optimal sowing time, which in turn affects farmers' preparations for the operation, if the weather conditions for the optimal sowing time do not change, the optimal sowing time within three days of the forecast day will not be adjusted.

[0068] The above technical solution provides a method for determining the sowing time of crops. By using meteorological data, the suitability of meteorological conditions is evaluated for the traditional empirical sowing period, thereby excluding sowing dates with unsuitable climatic conditions and obtaining suitable sowing dates with suitable climatic conditions. Subsequently, the remaining suitable sowing dates are traversed using a climate quality model for each crop variety based on strongly correlated meteorological data of each historical growth period to determine the sowing time that can achieve the best climate quality. Not only does it fully consider and utilize the meteorological conditions of each growth period, but it can also more accurately deduce the best sowing time for each crop variety, thereby achieving the purpose of improving crop quality.

[0069] In one embodiment, Figure 2 As shown, a flow chart of another method for determining the sowing time of crops is provided. Figure 2As shown, for any kind of rice to be sown, by obtaining historical rice detection data including historical meteorological data, comprehensive index of rice historical climate quality and historical growth period of rice, a climate quality model of the rice can be constructed. At the same time, the empirical sowing period of the rice variety can be obtained according to historical planting experience, and the meteorological forecast data of the planting area used to plant the rice variety during the empirical sowing period can be obtained through meteorological forecast, and the climatic conditions of each date in the empirical sowing period can be traversed to screen out the weather suitable sowing period suitable for sowing the rice variety from the empirical sowing period. The accumulated temperature method is used to traverse the weather suitable sowing period again to obtain the climate quality index sequence data within the sowing period. Thus, the rice climate quality of the planting area can be estimated based on the obtained growth period time series, the meteorological data of the suitable sowing period and the climate quality model corresponding to the rice variety, and the date corresponding to the maximum obtained climate quality index is determined as the optimal sowing time of the rice variety in the planting area.

[0070] This technical solution first uses weather forecast data to evaluate the suitability of meteorological conditions during traditional sowing periods, thereby excluding sowing dates with unsuitable climatic conditions. Subsequently, the established climate quality relationship model is used to traverse the remaining suitable sowing dates to determine the sowing time range that can achieve the best climate quality, and forecasts are pushed to farmers. This method not only helps prevent rice from being affected by meteorological disasters during the critical growth period, but also makes full use of meteorological conditions throughout the growth period, effectively improving the quality of rice and increasing farmers' economic benefits. The advantage of this method lies in its comprehensiveness and foresight. Through precise data analysis and prediction technology, it provides farmers with scientific and accurate sowing guidance to ensure that rice grows under optimal meteorological conditions, thereby achieving the production goal of high-quality rice.

[0071] An embodiment of the present application provides a storage medium on which a program is stored. When the program is executed by a processor, the method for determining the sowing time of crops is implemented.

[0072] An embodiment of the present application provides a processor, which is used to run a program, wherein the program executes the above-mentioned method for determining the crop sowing time when running.

[0073] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3As shown. The computer device includes a processor A01, a network interface A02, a memory (not shown in the figure) and a database (not shown in the figure) connected through a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02 and a database (not shown in the figure). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A04. The database of the computer device is used to store method data for determining the sowing time of crops. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program B02 is executed by the processor A01, a method for determining the sowing time of crops is implemented.

[0074] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0075] An embodiment of the present application provides a device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the method steps for determining the sowing time of crops are implemented.

[0076] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing the program steps of the method for initializing the determination of the crop sowing time.

[0077] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0078] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0079] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0080] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0081] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0082] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0083] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0084] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0085] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for determining crop sowing time, characterized in that: The method comprises: Acquire crop information of crops to be sown in the planting area, wherein the crop information includes the crop variety and growth period of the crops to be sown; Determine a date that meets preset conditions in the empirical sowing period based on the meteorological data of the planting area during the empirical sowing period of the crop to be sown, so as to obtain a suitable sowing period for the crop to be sown in the planting area; Determining a growth period time series for the crop to be sown based on the crop variety and the meteorological data of the suitable sowing period; Based on the growth period time series, the meteorological data of the suitable sowing period and the climate quality model corresponding to the crop variety, determine the climate quality index sequence of the planting area in the suitable sowing period, wherein, for any crop variety, the climate quality model of the crop variety is constructed based on the strongly correlated meteorological data of each growth period of the crop variety in a historical time period; The date corresponding to the maximum climate quality index is determined as the optimal sowing time for the crop to be sown in the planting area.

2. The method for determining crop sowing time according to claim 1, characterized in that: The meteorological data includes daily precipitation, daily wind force level and daily average temperature. The date that meets the preset conditions in the empirical sowing period is determined based on the meteorological data of the planting area during the empirical sowing period of the crop to be sown, so as to obtain the suitable sowing period of the crop to be sown in the planting area, including: Obtaining the daily precipitation, daily wind force level and daily average temperature for each date during the empirical sowing period; For any date, if the daily precipitation on the date is less than a first value, the daily wind force level on the date is less than a second value, and the difference between the daily average temperature on the date and the daily average temperatures of a plurality of consecutive adjacent dates is greater than a third value, it is determined that the date meets the preset condition; The date when the empirical sowing period meets the preset conditions is determined as the appropriate sowing period for the crop to be sown in the planting area.

3. The method for determining crop sowing time according to claim 1, characterized in that: For any crop variety, the climate quality model of the crop variety is shown in formula (1): Q=Σr s (β 0s +β 1s x 1s +β 2s x 2s +...+β ns x ns +ε) (1) Among them, Q is the climate quality index of crop varieties, β is , i∈(1,n) is the regression coefficient, x is , i∈(1,n) is the strongly correlated meteorological data of the growth period s of the crop variety, ε is a high-order infinitesimal error term, r s It is the contribution proportion corresponding to the growth period s of the crop variety.

4. The method for determining crop sowing time according to claim 1, characterized in that: For any crop variety, the method further comprises: Acquire historical planting information of the crop variety, the historical planting information including historical meteorological data of the crop variety in multiple historical time periods, multiple growth periods, sub-historical time periods corresponding to each growth period, and quality parameters in each historical time period, wherein each historical time period corresponds to a complete growth cycle of the crop variety, and the historical meteorological data includes the average daily temperature, the highest daily temperature, the lowest daily temperature, and sunshine hours of the crop variety in each historical time period; For each historical time period, determine the contribution ratio of each growth period of the crop variety, and determine the climate quality comprehensive index of the crop variety in the historical time period based on the quality parameters of the crop variety, so as to determine the stage climate quality comprehensive index corresponding to each growth period based on the contribution ratio of each growth period and the climate quality comprehensive index; Determine multiple average meteorological data corresponding to each growth period; For any growth period in any historical time period, the strongly correlated meteorological data of the growth period are determined based on the comprehensive index of the stage climate quality of the growth period, multiple average meteorological data, and the comprehensive index of the stage climate quality of all growth periods and historical meteorological data; Based on the strongly correlated meteorological data of each growth period and the comprehensive index of stage climate quality, the regression coefficient and high-order infinitesimal error term corresponding to each growth period are determined; A climate quality model for the crop variety is constructed based on the regression coefficient, high-order infinitesimal error term and contribution ratio corresponding to each growth period.

5. The method for determining crop sowing time according to claim 4, characterized in that: The strongly correlated meteorological data for each growth period are determined according to formula (2): ρ=cov(Q(s),x) / (σ Q(s) *σx) (2) Among them, ρ is the correlation coefficient, Q(s) is the comprehensive index of climate quality corresponding to any growth period, x is any average meteorological data of any growth period, σ Q(s) is the standard deviation of the comprehensive index of stage climate quality of all growth periods in the multiple historical time periods, and σx is the standard deviation of any historical meteorological data of all growth periods in the multiple historical time periods.

6. The method for determining crop sowing time according to claim 5, characterized in that: For any average meteorological data of any growth period, when the correlation coefficient corresponding to the average meteorological data is greater than a preset value, the average meteorological data is determined as the strongly correlated meteorological data of the growth period.

7. The method for determining crop sowing time according to claim 4, characterized in that: The regression coefficient and higher-order infinitesimal error term corresponding to each growth period are determined according to formula (3): Q(s)=β0+β1X1+β2X2+...+β n X n +e (3) Among them, β i , i∈(1,n) is the regression coefficient corresponding to any growth period, Q(s) is the comprehensive index of climate quality corresponding to any growth period, ε is the high-order infinitesimal error term corresponding to any growth period, x i ,i∈(1,n) is the strongly correlated meteorological data corresponding to any growth period.

8. A device for determining crop sowing time, characterized in that: include: a memory configured to store instructions; as well as A processor is configured to call the instructions from the memory and implement the method for determining crop sowing time according to any one of claims 1 to 7 when executing the instructions.

9. A system for determining crop sowing time, characterized in that: Comprising the device for determining crop sowing time according to claim 8.

10. A machine-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by a processor, the processor is configured to perform the method for determining crop sowing time according to any one of claims 1 to 7.

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